AI 中文总结
本文提出将最坏情况最优连接算法原生融入过滤器处理，扩展紧凑索引Ring以处理属性图，实验显示该方法性能优于多种基准系统。
AI 中文摘要
最坏情况最优（wco）连接算法在理论和实践中均展现出高效解决复杂基本图模式（BGPs）的能力。现代图查询语言（如SPARQL和GQL）以BGPs为核心，但还包含过滤器（即选择）等多种特征。这类条件通常在处理BGPs前后通过预过滤或后过滤处理。本文展示如何提升wco连接算法以原生融入此类过滤，从而提升效率。我们通过扩展Ring（一种紧凑索引，在图上仅需极少额外空间即可实现BGPs的wco解析），使其能运用新技巧处理属性图并保持紧凑性，以此证明该方法的优越性。我们实现了该扩展，实验表明其性能优于多种基准系统。
英文摘要
Worst-case-optimal (wco) join algorithms have demonstrated their power -- in both theory and practice -- to efficiently solve complex Basic Graph Patterns (BGPs). Modern graph query languages, such as SPARQL and GQL, have BGPs at their core, but also have a wide range of other features, including filters (aka.\ selections). Such conditions are typically handled via pre- or post-filtering, before or after processing the BGPs. In this paper we show how to uplift wco join algorithms so as to incorporate such filtering natively, improving efficiency. We demonstrate the superiority of this approach by extending the \textit{Ring} -- a compact index that provides wco resolution of BGPs within almost no extra space on top of the graph -- so as to handle property graphs using our new techniques while retaining compactness. We implement this extension and experimentally show that it outperforms various baseline systems.
